Student: Andrea Calafell

Advisors: Eva Mohedano (Insight), Kevin McGuinness (Insight), Noel E. O'Connor (Insight) and Xavier Giró-i-Nieto (UPC)

Program: Master in Computer Vision (Class of 2016)

Grade: A (9.0/10.0)

This thesis explores good practices for improving the detection of specific people in specific places. An approach combining recurrent and convolutional neural network have been considered to perform face detection. However, other more conventional methods have been tested, obtaining the best results by exploiting a deformable part model approach. A CNN is also used to obtain the face feature vectors and,  with the purpose of helping in the face recognition, an approach to perform query expansion has been also developed. Furthermore, in order to be able to evaluate the different configurations in our non-labelled dataset, a user interface has been used to annotate the images and be able to obtain a precision of the system. Finally, different fusion and normalization strategies has been explored with the aim of combining the scores obtained from the face recognition with the ones obtained in the place recognition.